Thesis opportunities- Weak Supervised Learning
In this project, you will develop a method to train an instance segmentation network based on images where we have a global class annotation. For instance, images labeled as containing either hammers or nails.
Training a segmentation network requires a lot of data and annotation efforts, often carried out by humans. This makes it costly and slow. If we could instead train a network based only on the global class of the objects contained in the image, this could be a much more efficient way to customize the network to new tasks.
Prerequisites
- Good understanding of deep learning of image analysis
- Practical experience of modern Deep Learning tools such as PyTorch, OpenVIVO or similar.
- Good knowledge of a suitable scripting language, preferably Python.
Contact
For more information about the position, contact:
Anders Moe, Algoritm Specialist – anders.moe@sick.se
Andreas Wrangsjö, R&D Team Manager – andreas.wrangsjo@sick.se or +46 728 530 320
or
Sarah Lantz, HR Business Partner, +46 739 10 99 37.
We warmly welcome your application — please submit it no later than October 26th.
SICK is a world-leading supplier of sensors and sensor solutions for industrial applications. We’re part of SICK AG — a global leader in sensor technology with 10,000 employees across 50 countries and headquarters in Freiburg, Germany. Together, we build technology that makes industries more efficient, intelligent, and safe.
As a Machine Vision Innovation Center, SICK Linköping develops high-performance cameras and advanced AI-powered software that drives the future of both manufacturing and logistics automation. Whether it’s helping robots pick the right item or enabling high-precision quality control with 2D and 3D vision, our solutions bring clarity, speed, and smart decision-making to complex industrial environments — all driven by a dedicated team of 100 colleagues.
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